Person re-identification (re-ID) aims at recognizing the identities of the people between different cameras. Here, deep da strategies can be helpful when trained models on one dataset are directly employed in another dataset. Deng et al. [1]} proposed Similarity Preserving Generative Adversarial Network (SPGAN). Inspired by Cycle gan [2]} and Siamese network, this solution translates in an unsupervised fashion the labeled source image into the target domain. Then it trains re-ID models with supervised learning that exploits the translated images. On the other hand, Xiao et al. [3]} introduced a dropout algorithm able to discard useless neurons under a guide provided by the characteristics of the domain.
